Our results suggest that despite the current standardized use of FIDs for evaluating dance generation models on the AIST++ dataset, these FID metrics, as they currently stand, are unreliable for measuring quality.
One potential explanation may be that the AIST++ test set does not thoroughly cover the train distribution given its very small size.
Additionally, due to data scarcity, both \(\text{FID}_k\)  and \(\text{FID}_g\)  depend on heuristic feature extractors that only compute superficial features of the data.
By contrast, FID-based metrics in data-rich domains such as image generation (where FID originated) enjoy the use of learned feature extractors (e.g. pre-trained ImageNet models), which are known to directly extract semantically meaningful features [1]}.
Indeed, in the text-to-motion domain, where significantly more paired data is available [2]}, the standard feature extractors for FID-style metrics depend on deep contrastively learned models [3]}, [4]}.
